Deep learning-based methods for person re-identification: A comprehensive review

In recent years, person re-identification (ReID) has received much attention since it is a fundamental task in intelligent surveillance systems and has widespread application prospects in numerous fields. Given an image of a pedestrian captured from one camera, the task is to identify this pedestrian from the gallery set captured by other multiple cameras. It is a challenging issue since the appearance of a pedestrian may suffer great changes across different cameras. The task has been greatly boosted by deep learning technology. There are mainly six types of deep learning-based methods designed for this issue, i.e. identification deep model, verification deep model, distance metric-based deep model, part-based deep model, video-based deep model and data augmentation-based deep model. In this paper, we first give a comprehensive review of current six types of deep learning methods. Second, we present the detailed descriptions of existing person ReID datasets. Then, some state-of-the-art performances of methods over recent years on several representative ReID datasets are summarized. Finally, we conclude this paper and discuss the future directions of the person ReID. (C) 2019 Elsevier B.V. All rights reserved.